1. bookVolume 31 (2021): Issue 3 (September 2021)
Journal Details
First Published
05 Apr 2007
Publication timeframe
4 times per year
access type Open Access

Forecasting Models for Chaotic Fractional–Order Oscillators Using Neural Networks

Published Online: 27 Sep 2021
Page range: 387 - 398
Received: 17 Mar 2021
Accepted: 06 Jul 2021
Journal Details
First Published
05 Apr 2007
Publication timeframe
4 times per year

This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing’s, Van der Pol’s, Tamaševičius’s and Chua’s, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model’s out-of-sample forecasting plots show the best tracking ability to approximate future state values.


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